对于声音场重建的微分物理
Samuel A Verburg1, Efren Fernandez-Grande2, Peter Gerstoft1
1Department of Electrical and Photonics Engineering, Technical University of Denmark (DTU), Kongens Lyngby, Denmark.
The Journal of the Acoustical Society of America
|November 21, 2025
概括
这项研究提出了一种新的可微分物理方法来重建声场. 它即使在有限的数据中也能实现准确的声场估计,优于传统的基于物理的神经网络.
科学领域:
- 声学 声学 在声学方面
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 声场重建从有限的空间观测中估计声场.
- 像物理信息神经网络 (PINNs) 这样的传统方法将物理纳入损失函数中.
- 严重的低采样给准确的声音场重建带来了挑战.
研究的目的:
- 为强大的声音场重建引入差分物理方法.
- 在数据稀缺条件下提高准确性和趋同.
- 在网络培训期间,强化物理作为强有力的约束.
主要方法:
- 用神经网络近似波方程初始条件.
- 使用微分算子的可微分数值解析器.
- 为了改善重建,纳入一种促进稀缺性的约束.
主要成果:
- 拟议的方法通过强制物理作为强制约束来实现稳定的网络训练.
- 在极端数据稀缺的情况下,证明了成功的声场重建.
- 在准确性和收性方面表现优于传统的基于物理的神经网络.
结论:
- 微分物理方法为声音场重建提供了稳定有效的方法.
- 这种技术在不足样本的场景中显著提高了性能.
- 它为现有的基于物理的神经网络方法提供了一个强大的替代方案.
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